9293734d35
## What changes were proposed in this pull request? This PR adds a new project ` external/kafka-0-10-sql` for Structured Streaming Kafka source. It's based on the design doc: https://docs.google.com/document/d/19t2rWe51x7tq2e5AOfrsM9qb8_m7BRuv9fel9i0PqR8/edit?usp=sharing tdas did most of work and part of them was inspired by koeninger's work. ### Introduction The Kafka source is a structured streaming data source to poll data from Kafka. The schema of reading data is as follows: Column | Type ---- | ---- key | binary value | binary topic | string partition | int offset | long timestamp | long timestampType | int The source can deal with deleting topics. However, the user should make sure there is no Spark job processing the data when deleting a topic. ### Configuration The user can use `DataStreamReader.option` to set the following configurations. Kafka Source's options | value | default | meaning ------ | ------- | ------ | ----- startingOffset | ["earliest", "latest"] | "latest" | The start point when a query is started, either "earliest" which is from the earliest offset, or "latest" which is just from the latest offset. Note: This only applies when a new Streaming query is started, and that resuming will always pick up from where the query left off. failOnDataLost | [true, false] | true | Whether to fail the query when it's possible that data is lost (e.g., topics are deleted, or offsets are out of range). This may be a false alarm. You can disable it when it doesn't work as you expected. subscribe | A comma-separated list of topics | (none) | The topic list to subscribe. Only one of "subscribe" and "subscribeParttern" options can be specified for Kafka source. subscribePattern | Java regex string | (none) | The pattern used to subscribe the topic. Only one of "subscribe" and "subscribeParttern" options can be specified for Kafka source. kafka.consumer.poll.timeoutMs | long | 512 | The timeout in milliseconds to poll data from Kafka in executors fetchOffset.numRetries | int | 3 | Number of times to retry before giving up fatch Kafka latest offsets. fetchOffset.retryIntervalMs | long | 10 | milliseconds to wait before retrying to fetch Kafka offsets Kafka's own configurations can be set via `DataStreamReader.option` with `kafka.` prefix, e.g, `stream.option("kafka.bootstrap.servers", "host:port")` ### Usage * Subscribe to 1 topic ```Scala spark .readStream .format("kafka") .option("kafka.bootstrap.servers", "host:port") .option("subscribe", "topic1") .load() ``` * Subscribe to multiple topics ```Scala spark .readStream .format("kafka") .option("kafka.bootstrap.servers", "host:port") .option("subscribe", "topic1,topic2") .load() ``` * Subscribe to a pattern ```Scala spark .readStream .format("kafka") .option("kafka.bootstrap.servers", "host:port") .option("subscribePattern", "topic.*") .load() ``` ## How was this patch tested? The new unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Author: Tathagata Das <tathagata.das1565@gmail.com> Author: Shixiong Zhu <zsxwing@gmail.com> Author: cody koeninger <cody@koeninger.org> Closes #15102 from zsxwing/kafka-source.
498 lines
12 KiB
Python
498 lines
12 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from functools import total_ordering
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import itertools
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import re
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all_modules = []
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@total_ordering
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class Module(object):
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"""
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A module is the basic abstraction in our test runner script. Each module consists of a set of
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source files, a set of test commands, and a set of dependencies on other modules. We use modules
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to define a dependency graph that lets determine which tests to run based on which files have
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changed.
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"""
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def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=(), environ={},
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sbt_test_goals=(), python_test_goals=(), blacklisted_python_implementations=(),
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test_tags=(), should_run_r_tests=False, should_run_build_tests=False):
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"""
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Define a new module.
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:param name: A short module name, for display in logging and error messages.
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:param dependencies: A set of dependencies for this module. This should only include direct
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dependencies; transitive dependencies are resolved automatically.
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:param source_file_regexes: a set of regexes that match source files belonging to this
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module. These regexes are applied by attempting to match at the beginning of the
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filename strings.
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:param build_profile_flags: A set of profile flags that should be passed to Maven or SBT in
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order to build and test this module (e.g. '-PprofileName').
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:param environ: A dict of environment variables that should be set when files in this
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module are changed.
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:param sbt_test_goals: A set of SBT test goals for testing this module.
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:param python_test_goals: A set of Python test goals for testing this module.
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:param blacklisted_python_implementations: A set of Python implementations that are not
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supported by this module's Python components. The values in this set should match
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strings returned by Python's `platform.python_implementation()`.
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:param test_tags A set of tags that will be excluded when running unit tests if the module
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is not explicitly changed.
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:param should_run_r_tests: If true, changes in this module will trigger all R tests.
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:param should_run_build_tests: If true, changes in this module will trigger build tests.
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"""
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self.name = name
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self.dependencies = dependencies
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self.source_file_prefixes = source_file_regexes
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self.sbt_test_goals = sbt_test_goals
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self.build_profile_flags = build_profile_flags
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self.environ = environ
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self.python_test_goals = python_test_goals
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self.blacklisted_python_implementations = blacklisted_python_implementations
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self.test_tags = test_tags
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self.should_run_r_tests = should_run_r_tests
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self.should_run_build_tests = should_run_build_tests
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self.dependent_modules = set()
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for dep in dependencies:
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dep.dependent_modules.add(self)
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all_modules.append(self)
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def contains_file(self, filename):
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return any(re.match(p, filename) for p in self.source_file_prefixes)
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def __repr__(self):
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return "Module<%s>" % self.name
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def __lt__(self, other):
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return self.name < other.name
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def __eq__(self, other):
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return self.name == other.name
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def __ne__(self, other):
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return not (self.name == other.name)
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def __hash__(self):
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return hash(self.name)
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tags = Module(
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name="tags",
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dependencies=[],
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source_file_regexes=[
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"common/tags/",
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]
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)
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catalyst = Module(
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name="catalyst",
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dependencies=[tags],
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source_file_regexes=[
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"sql/catalyst/",
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],
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sbt_test_goals=[
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"catalyst/test",
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],
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)
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sql = Module(
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name="sql",
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dependencies=[catalyst],
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source_file_regexes=[
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"sql/core/",
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],
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sbt_test_goals=[
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"sql/test",
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],
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)
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hive = Module(
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name="hive",
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dependencies=[sql],
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source_file_regexes=[
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"sql/hive/",
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"bin/spark-sql",
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],
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build_profile_flags=[
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"-Phive",
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],
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sbt_test_goals=[
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"hive/test",
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],
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test_tags=[
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"org.apache.spark.tags.ExtendedHiveTest"
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]
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)
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hive_thriftserver = Module(
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name="hive-thriftserver",
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dependencies=[hive],
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source_file_regexes=[
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"sql/hive-thriftserver",
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"sbin/start-thriftserver.sh",
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],
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build_profile_flags=[
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"-Phive-thriftserver",
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],
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sbt_test_goals=[
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"hive-thriftserver/test",
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]
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)
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sql_kafka = Module(
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name="sql-kafka-0-10",
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dependencies=[sql],
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source_file_regexes=[
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"external/kafka-0-10-sql",
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],
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sbt_test_goals=[
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"sql-kafka-0-10/test",
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]
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)
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sketch = Module(
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name="sketch",
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dependencies=[tags],
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source_file_regexes=[
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"common/sketch/",
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],
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sbt_test_goals=[
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"sketch/test"
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]
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)
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graphx = Module(
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name="graphx",
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dependencies=[tags],
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source_file_regexes=[
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"graphx/",
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],
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sbt_test_goals=[
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"graphx/test"
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]
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)
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streaming = Module(
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name="streaming",
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dependencies=[tags],
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source_file_regexes=[
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"streaming",
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],
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sbt_test_goals=[
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"streaming/test",
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]
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)
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# Don't set the dependencies because changes in other modules should not trigger Kinesis tests.
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# Kinesis tests depends on external Amazon kinesis service. We should run these tests only when
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# files in streaming_kinesis_asl are changed, so that if Kinesis experiences an outage, we don't
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# fail other PRs.
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streaming_kinesis_asl = Module(
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name="streaming-kinesis-asl",
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dependencies=[tags],
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source_file_regexes=[
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"external/kinesis-asl/",
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"external/kinesis-asl-assembly/",
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],
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build_profile_flags=[
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"-Pkinesis-asl",
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],
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environ={
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"ENABLE_KINESIS_TESTS": "1"
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},
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sbt_test_goals=[
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"streaming-kinesis-asl/test",
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]
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)
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streaming_kafka = Module(
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name="streaming-kafka-0-8",
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dependencies=[streaming],
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source_file_regexes=[
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"external/kafka-0-8",
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"external/kafka-0-8-assembly",
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],
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sbt_test_goals=[
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"streaming-kafka-0-8/test",
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]
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)
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streaming_flume_sink = Module(
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name="streaming-flume-sink",
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dependencies=[streaming],
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source_file_regexes=[
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"external/flume-sink",
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],
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sbt_test_goals=[
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"streaming-flume-sink/test",
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]
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)
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streaming_flume = Module(
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name="streaming-flume",
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dependencies=[streaming],
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source_file_regexes=[
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"external/flume",
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],
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sbt_test_goals=[
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"streaming-flume/test",
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]
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)
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streaming_flume_assembly = Module(
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name="streaming-flume-assembly",
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dependencies=[streaming_flume, streaming_flume_sink],
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source_file_regexes=[
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"external/flume-assembly",
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]
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)
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mllib_local = Module(
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name="mllib-local",
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dependencies=[tags],
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source_file_regexes=[
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"mllib-local",
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],
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sbt_test_goals=[
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"mllib-local/test",
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]
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)
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mllib = Module(
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name="mllib",
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dependencies=[mllib_local, streaming, sql],
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source_file_regexes=[
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"data/mllib/",
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"mllib/",
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],
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sbt_test_goals=[
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"mllib/test",
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]
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)
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examples = Module(
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name="examples",
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dependencies=[graphx, mllib, streaming, hive],
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source_file_regexes=[
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"examples/",
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],
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sbt_test_goals=[
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"examples/test",
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]
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)
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pyspark_core = Module(
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name="pyspark-core",
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dependencies=[],
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source_file_regexes=[
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"python/(?!pyspark/(ml|mllib|sql|streaming))"
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],
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python_test_goals=[
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"pyspark.rdd",
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"pyspark.context",
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"pyspark.conf",
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"pyspark.broadcast",
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"pyspark.accumulators",
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"pyspark.serializers",
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"pyspark.profiler",
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"pyspark.shuffle",
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"pyspark.tests",
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]
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)
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pyspark_sql = Module(
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name="pyspark-sql",
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dependencies=[pyspark_core, hive],
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source_file_regexes=[
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"python/pyspark/sql"
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],
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python_test_goals=[
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"pyspark.sql.types",
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"pyspark.sql.context",
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"pyspark.sql.session",
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"pyspark.sql.conf",
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"pyspark.sql.catalog",
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"pyspark.sql.column",
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"pyspark.sql.dataframe",
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"pyspark.sql.group",
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"pyspark.sql.functions",
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"pyspark.sql.readwriter",
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"pyspark.sql.streaming",
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"pyspark.sql.window",
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"pyspark.sql.tests",
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]
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)
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pyspark_streaming = Module(
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name="pyspark-streaming",
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dependencies=[
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pyspark_core,
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streaming,
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streaming_kafka,
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streaming_flume_assembly,
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streaming_kinesis_asl
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],
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source_file_regexes=[
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"python/pyspark/streaming"
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],
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python_test_goals=[
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"pyspark.streaming.util",
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"pyspark.streaming.tests",
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]
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)
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pyspark_mllib = Module(
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name="pyspark-mllib",
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dependencies=[pyspark_core, pyspark_streaming, pyspark_sql, mllib],
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source_file_regexes=[
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"python/pyspark/mllib"
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],
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python_test_goals=[
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"pyspark.mllib.classification",
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"pyspark.mllib.clustering",
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"pyspark.mllib.evaluation",
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"pyspark.mllib.feature",
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"pyspark.mllib.fpm",
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"pyspark.mllib.linalg.__init__",
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"pyspark.mllib.linalg.distributed",
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"pyspark.mllib.random",
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"pyspark.mllib.recommendation",
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"pyspark.mllib.regression",
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"pyspark.mllib.stat._statistics",
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"pyspark.mllib.stat.KernelDensity",
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"pyspark.mllib.tree",
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"pyspark.mllib.util",
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"pyspark.mllib.tests",
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],
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blacklisted_python_implementations=[
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"PyPy" # Skip these tests under PyPy since they require numpy and it isn't available there
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]
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)
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pyspark_ml = Module(
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name="pyspark-ml",
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dependencies=[pyspark_core, pyspark_mllib],
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source_file_regexes=[
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"python/pyspark/ml/"
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],
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python_test_goals=[
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"pyspark.ml.feature",
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"pyspark.ml.classification",
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"pyspark.ml.clustering",
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"pyspark.ml.linalg.__init__",
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"pyspark.ml.recommendation",
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"pyspark.ml.regression",
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"pyspark.ml.tuning",
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"pyspark.ml.tests",
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"pyspark.ml.evaluation",
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],
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blacklisted_python_implementations=[
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"PyPy" # Skip these tests under PyPy since they require numpy and it isn't available there
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]
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)
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sparkr = Module(
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name="sparkr",
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dependencies=[hive, mllib],
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source_file_regexes=[
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"R/",
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],
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should_run_r_tests=True
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)
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docs = Module(
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name="docs",
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dependencies=[],
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source_file_regexes=[
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"docs/",
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]
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)
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build = Module(
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name="build",
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dependencies=[],
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source_file_regexes=[
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".*pom.xml",
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"dev/test-dependencies.sh",
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],
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should_run_build_tests=True
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)
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yarn = Module(
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name="yarn",
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dependencies=[],
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source_file_regexes=[
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"yarn/",
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"common/network-yarn/",
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],
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build_profile_flags=["-Pyarn"],
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sbt_test_goals=[
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"yarn/test",
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"network-yarn/test",
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],
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test_tags=[
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"org.apache.spark.tags.ExtendedYarnTest"
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]
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)
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mesos = Module(
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name="mesos",
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dependencies=[],
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source_file_regexes=["mesos/"],
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build_profile_flags=["-Pmesos"],
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sbt_test_goals=["mesos/test"]
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)
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# The root module is a dummy module which is used to run all of the tests.
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# No other modules should directly depend on this module.
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root = Module(
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name="root",
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dependencies=[build], # Changes to build should trigger all tests.
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source_file_regexes=[],
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# In order to run all of the tests, enable every test profile:
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build_profile_flags=list(set(
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itertools.chain.from_iterable(m.build_profile_flags for m in all_modules))),
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sbt_test_goals=[
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"test",
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],
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python_test_goals=list(itertools.chain.from_iterable(m.python_test_goals for m in all_modules)),
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should_run_r_tests=True,
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should_run_build_tests=True
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)
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